| nonsmooth {nonsmooth} | R Documentation |
Nonparametric methods for smoothing nonsmooth data
Description
This package provides nonparametric methods for smoothing nonsmooth data. Change-point data is the intended application, with a focus on those with jumps in the regression function. Descriptions of the implementation of these methods can be found in Thompson, J.R.J. (2024).
Details
This package contains two additional functions for simulated one-D and two-D change-point data. This package also contains a real fire spread dataset from a micro-fire experiment. This data can be viewed as time dependent two-dimensional change-point data. The boundaries between fuel, burning and burn-out regions are seperated by two change-point curves. More information on experimentation and data can befound in Thompson, Wang, and Braun (2020) and Wang, Thompson, and Braun (2019).
Author(s)
John R.J. Thompson <john.thompson@ubc.ca>
Maintainer: John R.J. Thompson <john.thompson@ubc.ca>
I would like to acknowledge funding support from the University of British Columbia Aspire Fund (UBC:www.ok.ubc.ca/).
References
Thompson, J.R.J. (2024) “Iterative Smoothing for Change-point Regression Function Estimation”, Journal of Applied Statistics, 1-25. <doi:10.1080/02664763.2024.2352759>
Thompson, J.R.J., Wang, X.J., & Braun, W.J. (2020) “A mouse model for studying fire spread rates using experimental micro-fires”, Journal of Environmental Statistics, 9(1), 1-19. <[https://www.jenvstat.org/v09/i06]https://www.jenvstat.org/v09/i06>
Wang, X.J., Thompson, J.R.J., Braun, W.J., & Woolford, D.G. (2019) “Fitting a stochastic fire spread model to data.” Advances in Statistical Climatology, Meteorology and Oceanography, 5(1), 57-66. <[https://ascmo.copernicus.org/articles/5/57/2019/]https://ascmo.copernicus.org/articles/5/57/2019/>